SLIDER: A Generic Metaheuristic for the Discovery of Correlated Motifs in Protein-Protein Interaction Networks
Issue No. 05 - September/October (2011 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.17
Aalt D.J. van Dijk , Applied Bioinformatics - Plant Research International, Wageningen
Roeland C.H.J. van Ham , Applied Bioinformatics - Plant Research International, Wageningen
Dries Van Dyck , Hasselt University, Diepenbeek and Transnational University of Limburg
Peter Boyen , Hasselt University, Diepenbeek and Transnational University of Limburg
Frank Neven , Hasselt University, Diepenbeek and Transnational University of Limburg
Correlated motif mining (cmm) is the problem of finding overrepresented pairs of patterns, called motifs, in sequences of interacting proteins. Algorithmic solutions for cmm thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network. We experimentally establish the superiority of the Chi-square-based support measure over other support measures. Furthermore, we obtain that cmm is an np-hard problem for a large class of support measures (including Chi-square) and reformulate the search for correlated motifs as a combinatorial optimization problem. We then present the generic metaheuristic slider which uses steepest ascent with a neighborhood function based on sliding motifs and employs the Chi-square-based support measure. We show that slider outperforms existing motif-driven cmm methods and scales to large protein-protein interaction networks. The slider-implementation and the data used in the experiments are available on http://bioinformatics.uhasselt.be.
Graphs and networks, biology and genetics.
Aalt D.J. van Dijk, Roeland C.H.J. van Ham, Dries Van Dyck, Peter Boyen, Frank Neven, "SLIDER: A Generic Metaheuristic for the Discovery of Correlated Motifs in Protein-Protein Interaction Networks", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 1344-1357, September/October 2011, doi:10.1109/TCBB.2011.17